multissim3

Multiscale structural similarity (MS-SSIM) index for volume quality

Since R2020a

Syntax

score = multissim3(V,Vref)

score = multissim3(V,Vref,Name,Value)

[score,qualityMaps] = multissim3(___)

Description

score = multissim3(V,Vref) calculates the multiscale structural similarity (MS-SSIM) index, score, for volume V, using Vref as the reference volume. A value closer to 1 indicates better quality and a value closer to 0 indicates poorer quality.

The 3-D MS-SSIM operation is defined for grayscale volumes. For inputs with more than three dimensions, multissim3 treats each element of higher dimensions as separate 3-D grayscale volumes. multissim3 treats 2-D RGB images as 3-D grayscale volumes. To calculate the MS-SSIM of color channels in an RGB image, use the multissim function.

example

score = multissim3(V,Vref,Name,Value) controls aspects of the computation using one or more name-value arguments. For example, specify the number of scales using the 'NumScales' argument.

example

[score,qualityMaps] = multissim3(___) also returns the local MS-SSIM index value for each voxel in V, and each of the scaled versions of V. The qualityMaps output is a cell array containing maps for each of the scaled versions of V, with each quality map the same size as the corresponding scaled version.

example

Examples

collapse all

Calculate the MS-SSIM Index for Volume

Open Live Script

Load a 3-D volume into the workspace.

load mri D
Vref = squeeze(D);

Create a noisy version of the original volume for quality measurement comparison purposes.

V = imnoise(Vref,'salt & pepper',0.05);

Calculate the MS-SSIM index that measures the quality of the input volume compared to the reference volume.

score = multissim3(V,Vref)

score = single

0.7261

Calculate MS-SSIM and Retrieve Local Structural Similarity Maps

Open Live Script

Load a volume into the workspace. This volume will be the reference volume. Create a copy of the reference volume.

load mri D
Vref = squeeze(D);
V = Vref;

Add noise to a localized part of the volume for quality comparison purposes.

V(1:100,1:100,1:10) = imnoise(Vref(1:100,1:100,1:10),"salt & pepper",0.05);
figure
sliceViewer(V,"Parent",figure);

Calculate the MS-SSIM index for the volumes and retrieve the local structural similarity maps. The multissim3 function returns qualitymaps, a cell array containing a local structural similarity map for each of the scaled versions of the volume. In the quality map, the value 1 indicates the highest quality.

[score, qualitymaps] = multissim3(V,Vref);
figure
sliceViewer(V,"Parent",figure);

Calculate MS-SSIM Specifying Weights for Each Scaled Volume

Open Live Script

Load a volume into the workspace.

load mri D
Vref = squeeze(D);

Create a noisy version of the volume for quality measurement comparison purposes.

V = imnoise(Vref,'salt & pepper',0.05);

Calculate the MS-SSIM index for the noisy volume, using the original volume as the reference. Specify how much to weigh the local MS-SSIM index calculations for each scaled volume using the 'ScaleWeights' argument. The example uses the weights defined in the article by Wang, Simoncelli, and Bovik.

score = multissim3(V,Vref,'ScaleWeights',[0.0448,0.2856,0.3001,0.2363,0.1333]);

Calculate MS-SSIM of 3-D Volume Stack

Open Live Script

Read a volumetric image into the workspace.

VRef = load("mristack.mat");
VRef = im2single(VRef.mristack);

Simulate a batch of six images by replicating the image along the fourth dimension.

VRefBatch = repmat(VRef,[1 1 1 6]);

Create a version of the image stack with added salt and pepper noise.

VNoisyBatch = imnoise(VRefBatch,"salt & pepper");

Calculate the MS-SSIM of each volume in the stack.

score = multissim3(VNoisyBatch,VRefBatch)

score = 1x1x1x6 single array
score(:,:,1,1) =

    0.8341


score(:,:,1,2) =

    0.8347


score(:,:,1,3) =

    0.8337


score(:,:,1,4) =

    0.8333


score(:,:,1,5) =

    0.8348


score(:,:,1,6) =

    0.8343

Calculate MS-SSIM for 3-D `dlarray` Input

This example uses:

Open Live Script

Read a volumetric image into the workspace.

VRef = load("mristack.mat");
VRef = im2single(VRef.mristack);

Create a copy of the batch of images, adding salt and pepper noise.

Vnoisy = imnoise(VRef,"salt & pepper");

Create unformatted dlarray objects for the original and noisy batch of images.

dlref = dlarray(VRef);
dlnoisy = dlarray(Vnoisy);

Calculate the MS-SSIM score of the noisy data with respect to the original data.

score = multissim3(dlnoisy,dlref)

score = 
  1x1 single dlarray

    0.8341

Input Arguments

collapse all

`V` — Input volume
numeric array | `dlarray` object

Input volume, specified as a numeric array of three or more dimensions or a dlarray (Deep Learning Toolbox) object. Formatted dlarray objects cannot include more than one channel label, more than one batch label, and more than three spatial labels.

Data Types: single | double | int16 | uint8 | uint16

`Vref` — Reference volume
numeric array

Reference volume, specified as a numeric array of three or more dimensions or a dlarray (Deep Learning Toolbox) object. Formatted dlarray objects cannot include more than one channel label, more than one batch label, and more than three spatial labels. The reference volume must be of the same size and data type as the input volume, V.

Data Types: single | double | int16 | uint8 | uint16

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: score = multissim3(V,Vref,'NumScales',3);

`NumScales` — Number of scales
`5` (default) | positive integer

Number of scales used to calculate MS-SSIM, specified as the comma-separated pair consisting of 'NumScales' and a positive integer. Setting 'NumScales' to 1 is equivalent to the use of the ssim function with the 'Exponents' name-value pair argument set to [1 1 1]. The size of the input volume limits the number of scales. The multissim3 function scales the volume (NumScales - 1) times, downsampling the volume by a factor of 2 with each scaling.

`ScaleWeights` — Relative values across scales
vector of positive numbers

Relative values across the scales, specified as the comma-separated pair consisting of 'ScaleWeights' and a vector of positive numbers. The length of the vector is equal to the number of scales, because each element corresponds to one of the scaled versions of the original volume. The multissim3 function normalizes the values to 1. By default, the scale weights equal fspecial('gaussian',[1,numScales],1). The multissim3 function uses a Gaussian distribution as the default because the human visual sensitivity peaks at middle frequencies and decreases in both directions. For an example of setting 'ScaleWeights', see Calculate MS-SSIM Specifying Weights for Each Scaled Volume.

`Sigma` — Standard deviation
`1.5` (default) | positive number

Standard deviation of the isotropic Gaussian function, specified as the comma-separated pair consisting of 'Sigma' and a positive number. This value specifies the weighting of the neighborhood voxels around a voxel for estimating local statistics. The multissim3 function uses this weighting to avoid blocking artifacts in estimating local statistics.

`DynamicRange` — Dynamic range of input volume
positive number

Dynamic range of the input volume, specified as a positive number. The default value of DynamicRange depends on the data type of volume V, and is calculated as diff(getrangefromclass(V)). For example, the default dynamic range is 255 for volumes of data type uint8, and the default is 1 for volumes of data type double or single with voxel values in the range [0, 1].

Output Arguments

collapse all

`score` — MS-SSIM index
numeric scalar | numeric array | `dlarray` object

MS-SSIM index for image quality, returned as a numeric scalar, numeric array, or dlarray (Deep Learning Toolbox) object as indicated in the table. The value of score is typically in the range [0, 1]. The value 1 indicates the highest quality and occurs when V and Vref are equivalent. Smaller values correspond to poorer quality. For some combinations of inputs and name-value pair arguments, score can be negative.

Input Volume Type	MS-SSIM Value
3-D numeric matrices	Numeric scalar with a single MS-SSIM measurement.
3-D `dlarray` objects	1-by-1 `dlarray` object with a single MS-SSIM measurement.
N-D numeric arrays with N>3	Numeric array of the same dimensionality as the input volumes. The first three dimensions of `score` are singleton dimensions. There is one MS-SSIM measurement for each element along the higher dimensions.
Unformatted N-D `dlarray` objects with N>3	`dlarray` object of the same dimensionality as the input volumes. The first three dimensions of `score` are singleton dimensions. There is one MS-SSIM measurement for each element along the higher dimensions.
Formatted N-D `dlarray` objects with N>3	`dlarray` object of the same dimensionality as the input volumes. The spatial dimensions of `score` are singleton dimensions. There is one MS-SSIM measurement for each element along any channel or batch dimension.

`qualityMaps` — Local MS-SSIM index values
cell array of numeric arrays | cell array of `dlarray` objects

Local MS-SSIM index values for each pixel in each scaled version, returned as a cell array of numeric arrays or a cell array of dlarray (Deep Learning Toolbox) objects. The size of the cell array is 1-by-NumScales. Each element in qualityMaps indicates the quality of the corresponding pixel at the corresponding scale factor. The format of each element uses the formatting of the score argument, based on the format of the input volumes.

Algorithms

The structural similarity (SSIM) index measures perceived quality by quantifying the structural similarity between a volume and a reference volume (see ssim). The multissim3 function calculates the MS-SSIM by combining the SSIM index of several versions of the volume at various scales. The MS-SSIM index can be more robust when compared to the SSIM index with regard to variations in viewing conditions.

The multissim3 function uses double-precision arithmetic for input volumes of class double. All other types of input volumes use single-precision arithmetic.

References

[1] Wang, Z., Simoncelli, E.P., Bovik, A.C. Multiscale Structural Similarity for Image Quality Assessment. In: The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, 1398–1402. Pacific Grove, CA, USA: IEEE, 2003. https://doi.org/10.1109/ACSSC.2003.1292216.

Extended Capabilities

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Image Processing on a GPU.

Version History

Introduced in R2020a

expand all

R2022b: Support for thread-based environments

multissim3 now supports thread-based environments.

R2021a: Calculate metrics of deep learning arrays and specify dimensions of computation

The multissim3 function now accepts dlarray input for deep learning applications.

This function also supports formatted data with dimension labels of 'S' (spatial), 'C' (channel), and 'B' (batch). For data with a batch dimension, the function returns a separate result for each index along the batch dimension.

R2021a: Support for GPU acceleration

multissim3 now supports GPU acceleration (requires Parallel Computing Toolbox™).

multissim3

Syntax

Description

Examples

Calculate the MS-SSIM Index for Volume

Calculate MS-SSIM and Retrieve Local Structural Similarity Maps

Calculate MS-SSIM Specifying Weights for Each Scaled Volume

Calculate MS-SSIM of 3-D Volume Stack

Calculate MS-SSIM for 3-D `dlarray` Input

Input Arguments

`V` — Input volume
numeric array | `dlarray` object

`Vref` — Reference volume
numeric array

Name-Value Arguments

`NumScales` — Number of scales
`5` (default) | positive integer

`ScaleWeights` — Relative values across scales
vector of positive numbers

`Sigma` — Standard deviation
`1.5` (default) | positive number

`DynamicRange` — Dynamic range of input volume
positive number

Output Arguments

`score` — MS-SSIM index
numeric scalar | numeric array | `dlarray` object

`qualityMaps` — Local MS-SSIM index values
cell array of numeric arrays | cell array of `dlarray` objects

Algorithms

References

Extended Capabilities

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

R2022b: Support for thread-based environments

R2021a: Calculate metrics of deep learning arrays and specify dimensions of computation

R2021a: Support for GPU acceleration

See Also

Topics

multissim3

Syntax

Description

Examples

Calculate the MS-SSIM Index for Volume

Calculate MS-SSIM and Retrieve Local Structural Similarity Maps

Calculate MS-SSIM Specifying Weights for Each Scaled Volume

Calculate MS-SSIM of 3-D Volume Stack

Calculate MS-SSIM for 3-D dlarray Input

Input Arguments

V — Input volume numeric array | dlarray object

Vref — Reference volume numeric array

Name-Value Arguments

NumScales — Number of scales 5 (default) | positive integer

ScaleWeights — Relative values across scales vector of positive numbers

Sigma — Standard deviation 1.5 (default) | positive number

DynamicRange — Dynamic range of input volume positive number

Output Arguments

score — MS-SSIM index numeric scalar | numeric array | dlarray object

qualityMaps — Local MS-SSIM index values cell array of numeric arrays | cell array of dlarray objects

Algorithms

References

Extended Capabilities

Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

R2022b: Support for thread-based environments

R2021a: Calculate metrics of deep learning arrays and specify dimensions of computation

R2021a: Support for GPU acceleration

See Also

Topics

Calculate MS-SSIM for 3-D `dlarray` Input

`V` — Input volume
numeric array | `dlarray` object

`Vref` — Reference volume
numeric array

`NumScales` — Number of scales
`5` (default) | positive integer

`ScaleWeights` — Relative values across scales
vector of positive numbers

`Sigma` — Standard deviation
`1.5` (default) | positive number

`DynamicRange` — Dynamic range of input volume
positive number

`score` — MS-SSIM index
numeric scalar | numeric array | `dlarray` object

`qualityMaps` — Local MS-SSIM index values
cell array of numeric arrays | cell array of `dlarray` objects

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.